Isakhani, HamidAouf, NabilKechagias-Stamatis, OdysseasWhidborne, James F.2018-10-192018-10-192018-07-23Isakhani H, Aouf N, Kechagias-Stamatis O, Whidborne J. (2020) A furcated visual collision avoidance system for an autonomous micro robot. IEEE Transactions on Cognitive and Developmental Systems, Volume 12, March 2020, pp. 1-112379-8920https://doi.org/10.1109/TCDS.2018.2858742http://dspace.lib.cranfield.ac.uk/handle/1826/13550This paper proposes a secondary reactive collision avoidance system for micro class of robots based on a novel approach known as the Furcated Luminance-Difference Processing (FLDP) inspired by the Lobula Giant Movement Detector, a wide-field visual neuron located in the lobula layer of a locust nervous system. This paper addresses some of the major collision avoidance challenges; obstacle proximity & direction estimation, and operation in GPS-denied environment with irregular lighting. Additionally, it has proven effective in detecting edges independent of background color, size, and contour. The FLDP executes a series of image enhancement and edge detection algorithms to estimate collision threat-level which further determines whether or not the robot’s field of view must be dissected where each section’s response is compared against the others to generate a simple collision-free maneuver. Ultimately, the computation load and the performance of the model is assessed against an eclectic set of off-line as well as real-time real-world collision scenarios validating the proposed model’s asserted capability to avoid obstacles at more than 670 mm prior to collision, moving at 1.2 ms¯¹ with a successful avoidance rate of 90% processing at 120 Hz on a simple single core microcontroller, sufficient to conclude the system’s feasibility for real-time real-world applications that possess fail-safe collision avoidance system.enAttribution-NonCommercial 4.0 Internationalhttp://creativecommons.org/licenses/by-nc/4.0/Autonomous robotsbiologically-inspiredfurcated luminance-difference processing (FLDP)direction and proximity estimationArticle21745606